Estimating fish energy content and gain from length and wet weight

Abstract

Modeling energy content and gain of individuals is of increasing importance in ecosystem modeling, especially in aquaculture and fisheries. Traditional models for estimating the content and gain are either imprecise or expensive, in part because of intensive data requirements. Here we show how routine biometric data (length and wet weight or condition index) can be used to estimate total energy content of fish. Starting with theoretical partitioning between structure and reserves, we create a model to relate energy to the Fulton's condition index. We then use data from cultured sea bream (Sparus aurata L.) to show that the model based on structure should be used to calculate energy content from biometric data. Validation using independent data shows remarkable ability of the model to predict energy content (R2>0.99), while comparison with previously used models demonstrates marked differences in predictions when fish condition is variable. Unlike traditional methods, our model predicts different energy content and gain (or loss) for small fat and large thin fish of equal weight, and can therefore give considerable additional value to biometric data commonly collected in aquaculture, fisheries, and related citizen science programs.